{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/data/jupyter_root/ZJ/data.csv')\n",
    "df_clean=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70327, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 方法1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "bad      0\n",
       "good    35\n",
       "Name: email_prefix, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var=df_clean.groupby('label').email_prefix.count()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('label')\n",
    "ax1.set_ylabel('email_prefix')\n",
    "ax1.set_title(' ')\n",
    "\n",
    "var.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 方法2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percent label\n",
       "0      0.9     0\n",
       "1      0.1     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'percent':[0.9,0.1],'label': ['0','1']})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 1.5, 0, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.concat([df['percent'],df['label']],axis=1)\n",
    "sns.barplot(x='label',y='percent',data=data).axis(ymin=0, ymax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06192652329749107, 1.0619265232974908, 0, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='label',y='percent',data=data).axis(ymin=0, ymax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "300.545px",
    "left": "793.636px",
    "right": "20px",
    "top": "120px",
    "width": "350px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
